Research Article: Automatic characterization of stride parameters in canines with a single wearable inertial sensor

Date Published: June 14, 2018

Publisher: Public Library of Science

Author(s): Gregory J. Jenkins, Chady H. Hakim, N. Nora Yang, Gang Yao, Dongsheng Duan, Yih-Kuen Jan.

http://doi.org/10.1371/journal.pone.0198893

Abstract

Gait analysis is valuable for studying neuromuscular and skeletal diseases. Wearable motion sensors or inertial measurement units (IMUs) have become common for human gait analysis. Canines are important large animal models for translational research of human diseases. Our objective is to develop a method for accurate and reliable determination of the timing of each stride in dogs using a wearable IMU.

We built a wireless IMU sensor using off-the-shelf components. We also developed a MATLAB algorithm for data acquisition and stride timing determination. Stride parameters from 1,259 steps of three adult mixed breed dogs were determined across a range of six height-normalized speeds using the IMU system. The IMU results were validated by frame-by-frame manual counting of high-speed video recordings.

Comparing IMU derived results with video revealed that the mean error ± standard deviation for stride, stance, and swing duration was 0.001 ± 0.025, -0.001 ± 0.030, and 0.001 ± 0.019 s respectively. A mean error ± standard deviation of 0.000 ± 0.020 and -0.008 ± 0.027 s was obtained for determining toe-off and toe-touch events respectively. Only one step was missed by the algorithm in the video dataset of 1,259 steps.

We have developed and validated an IMU method for automatic canine gait analysis. Our method can be used for studying neuromuscular diseases in veterinary clinics and in translational research.

Partial Text

Gait analysis is valuable for diagnosis and assessing disease progression/therapy response in diseases that alter gait, such as Duchenne muscular dystrophy [1]. Observer-based examination has conventionally been used in gait assessment and is standard in clinic diagnosis [2]. However, subjective approaches are difficult to generate precise measurements. Quantitative methods such as optical recording systems, ground force plates/pressure walkways, and electromyography have become more popular in gait analysis [3]. However, these technologies require expensive and sophisticated instrumentation and facilities which limit their access for routine application. Low-end motion capture systems, such as OptiTrack (NaturalPoint, OR, USA), may cost ~$15,000 USD; while high-end video systems such as the Vicon system (Vicon, Oxford, UK) may run more than $200,000 USD [4]. Pressure walkways such as the GAIT4Dog system (CIR Systems, NJ, USA) may cost ~$25,000 USD.

To develop an automatic method that can accurately detect the swing and stance phase of a gait, we evaluated signals from the sensor. Characteristic patterns were observed in all axes from both accelerometer and gyroscope (Fig 2). To guide programming, a subset of video data was used to correlate the signal pattern and real-life dog gait. A broad Rz peak was consistently observed in every step. Hence, this was used as the step identifier. To detect swing and stance start, we explored a series of signal features from every axis. The preliminary algorithm was then tested in the whole video collection for the best fit. Feedback was then used to optimize the algorithm. Through iterative cycles of data fitting, we identified the best signal markers for detecting the swing and stance start (Fig 3).

Gait analysis systems are beneficial to researchers of neuromuscular and skeletal diseases, and the canine is an important model for translational research of human diseases [6, 20]. State-of-the-art methods such as pressure walkways and video motion-capture are highly capable of measuring canine gait [21]; yet, wearable IMUs offer distinct advantages due to their unique capabilities [22]. Specifically, pressure walkways are disadvantaged in that they only obtain gait characteristics at the 2D surface of the walkway; whereas IMUs obtain complete 3D motion even while the leg is off the ground. Video motion-capture is disadvantaged because it can only obtain a few strides at a time (unless coupled with a treadmill) and it requires a time-consuming setup to precisely attach anatomical markers. Additionally, video methods often obtain side-view 2D analysis only. A true 3D analysis requires a dedicated testing facility and complex multi-camera setups. Considering the low-cost, simplicity, capability, and added benefit of wearable IMUs to function in any environment, a sufficient IMU system would be able to achieve the same functionality in all existing gait analysis systems.

A wearable IMU method with sufficient capability could be preferable to state-of-the-art methods due to the low-cost and convenience of IMUs. We have developed an automated step phase timing detection method for wearable IMU-based dog gait analysis. Our IMU system offers high precision at 100 Hz data acquisition and obtained stride phase timing within a very small margin of error. To demonstrate whether our results were consistent with findings from field-standard techniques, we evaluated the correlation between step duration and the Froude number as well as the distribution of the duty factor over gait type. We found our results were remarkably similar to those that used pressure walkway or video recording techniques. Further development of our method is warranted for future application in veterinary clinics and in preclinical studies of neuromuscular diseases using the canine model.

 

Source:

http://doi.org/10.1371/journal.pone.0198893

 

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